harden delivery pipeline and production runtime

backend/routes/ml.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 import logging
 from datetime import datetime, timezone
-from typing import List, Sequence
+from collections.abc import Sequence
 
 from fastapi import APIRouter, FastAPI, HTTPException, Request, status
 from pydantic import BaseModel, Field
@@ -24,7 +24,7 @@ class HealthResponse(BaseModel):
 class PredictRequest(BaseModel):
     model_id: str = Field(..., alias="modelId")
     sensor_id: str
-    values: dict
+    values: dict[str, float]
 
 
 class PredictResponse(BaseModel):
@@ -42,7 +42,7 @@ class BatchResponse(BaseModel):
 
 
 class ModelsResponse(BaseModel):
-    models: List[str]
+    models: list[str]
 
 
 @router.get("/health", response_model=HealthResponse, status_code=200)
@@ -82,7 +82,7 @@ def predict(payload: PredictRequest, request: Request) -> PredictResponse:
 def predict_batch(payload: BatchRequest, request: Request) -> BatchResponse:
     registry = _require_registry(request)
     predictor = Predictor(registry=registry)
-    responses: List[PredictResponse] = []
+    responses: list[PredictResponse] = []
     for item in payload.requests:
         vector = FeatureVector(sensor_id=item.sensor_id, values=item.values)
         try:
@@ -111,7 +111,6 @@ def _require_registry(request: Request) -> ModelRegistry:
 
 
 def init_ml_routes(app: FastAPI, model_store: str = ".model_store") -> None:
-    registry = ModelRegistry(model_store)
-    app.state.registry = registry
+    app.state.model_store = model_store
     app.include_router(router)
     logger.info("ML routes registered")